Open Access

Algorithms for Hardware-Based Pattern Recognition

EURASIP Journal on Advances in Signal Processing20042004:642357

DOI: 10.1155/S1110865704404247

Received: 27 August 2003

Published: 29 September 2004

Abstract

Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are established in signal processing. They have proved to be excellent tools in feature extraction and classification. In this paper, we will present a hardware-accelerated image processing and classification system which is implemented on one field-programmable gate array (FPGA). Nonlinear discrete circular transforms generate a feature vector. The features are analyzed by a fuzzy classifier. This principle can be used for feature extraction, pattern recognition, and classification tasks. Implementation in radix-2 structures is possible, allowing fast calculations with a computational complexity of up to . Furthermore, the pattern separability properties of these transforms are better than those achieved with the well-known method based on the power spectrum of the Fourier Transform, or on several other transforms. Using different signal flow structures, the transforms can be adapted to different image and signal processing applications.

Keywords and phrases

image processing nonlinear circular transforms feature extraction fuzzy pattern recognition

Authors’ Affiliations

(1)
Koenig & Bauer AG (KBA), Bielefeld
(2)
Circuit and System Design Group, Technical University of Chemnitz

Copyright

© Lohweg et al. 2004